WGSN
WGSN INstock
WGSN's fashion big data analysis tool, providing users with accurate information on current market trends and retailer activity.
Background and context
Launched in late 2013, WGSN INstock gathers millions of eCommerce data points daily, aggregating them to give clients high-level insights across the fashion retail market.
By scraping product information from major fashion eCommerce sites, it enables brands to identify market trends in order to make better-informed buying, design and pricing decisions.
Role and approach
As UI Designer, I worked alongside a senior UX User Researcher to define user requirements, user journeys and information architecture to deliver desgins for this innovative product.
My previous experience as a retail buyer at Harrods really helped to define the product and data that buyers would be wanting. I therefore had a big hand in shaping the product scope.
The team operated in tightly defined sprints with a dedicated development team — this was WGSN's first big data product, marking a major development in their product offering.
Outcome
As a stand-alone product, it became an instant revenue generator as an additional subscription offer. Many existing WGSN clients quickly adopted this data insight product, leading to a multi-million £ increase in revenue.
WGSN INstock was named Silver Winner for Best New Product of the Year (Enterprise) at the Best in Biz Awards 2014. It went on to win Silver at Fashionbiz's annual product awards and was shortlisted for Best Tech Start-up 2015 by Drapers Magazine.
Due to the sheer volume of data available, accurate onboarding was vitally important. By focusing on a users specific job role and interest, relevant data, insight and comp shopper listings could be presented to each user - based on their specific needs.
Data sets on product categories, colour ranges, pricing bands and brand count provided valuable insight into both emerging trends, and trends that had already peaked.
Providing real-time retail market intelligence to Retail Buyers and Merchandisers, WGSN INstock gathered millions of retailer data points on a daily basis. Product data from 100's of retailer websites would be gathered to provide users with current market trends.
With daily data scrapes, this enabled WGSN INstock to build up a rich history of pricing and in-and-out-of-stock products, providing further insight into successful product lines.
